# Ultralytics YOLO 🚀, GPL-3.0 license from copy import copy import torch import torch.nn as nn from ultralytics.nn.tasks import PoseModel from ultralytics.yolo import v8 from ultralytics.yolo.utils import DEFAULT_CFG from ultralytics.yolo.utils.loss import KeypointLoss from ultralytics.yolo.utils.metrics import OKS_SIGMA from ultralytics.yolo.utils.ops import xyxy2xywh from ultralytics.yolo.utils.plotting import plot_images, plot_results from ultralytics.yolo.utils.tal import make_anchors from ultralytics.yolo.utils.torch_utils import de_parallel from ultralytics.yolo.v8.detect.train import Loss # BaseTrainer python usage class PoseTrainer(v8.detect.DetectionTrainer): def __init__(self, cfg=DEFAULT_CFG, overrides=None): if overrides is None: overrides = {} overrides['task'] = 'pose' super().__init__(cfg, overrides) def get_model(self, cfg=None, weights=None, verbose=True): model = PoseModel(cfg, ch=3, nc=self.data['nc'], data_kpt_shape=self.data['kpt_shape'], verbose=verbose) if weights: model.load(weights) return model def set_model_attributes(self): super().set_model_attributes() self.model.kpt_shape = self.data['kpt_shape'] def get_validator(self): self.loss_names = 'box_loss', 'pose_loss', 'kobj_loss', 'cls_loss', 'dfl_loss' return v8.pose.PoseValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args)) def criterion(self, preds, batch): if not hasattr(self, 'compute_loss'): self.compute_loss = PoseLoss(de_parallel(self.model)) return self.compute_loss(preds, batch) def plot_training_samples(self, batch, ni): images = batch['img'] kpts = batch['keypoints'] cls = batch['cls'].squeeze(-1) bboxes = batch['bboxes'] paths = batch['im_file'] batch_idx = batch['batch_idx'] plot_images(images, batch_idx, cls, bboxes, kpts=kpts, paths=paths, fname=self.save_dir / f'train_batch{ni}.jpg') def plot_metrics(self): plot_results(file=self.csv, pose=True) # save results.png # Criterion class for computing training losses class PoseLoss(Loss): def __init__(self, model): # model must be de-paralleled super().__init__(model) self.kpt_shape = model.model[-1].kpt_shape self.bce_pose = nn.BCEWithLogitsLoss() is_pose = self.kpt_shape == [17, 3] nkpt = self.kpt_shape[0] # number of keypoints sigmas = torch.from_numpy(OKS_SIGMA).to(self.device) if is_pose else torch.ones(nkpt, device=self.device) / nkpt self.keypoint_loss = KeypointLoss(sigmas=sigmas) def __call__(self, preds, batch): loss = torch.zeros(5, device=self.device) # box, cls, dfl, kpt_location, kpt_visibility feats, pred_kpts = preds if isinstance(preds[0], list) else preds[1] pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split( (self.reg_max * 4, self.nc), 1) # b, grids, .. pred_scores = pred_scores.permute(0, 2, 1).contiguous() pred_distri = pred_distri.permute(0, 2, 1).contiguous() pred_kpts = pred_kpts.permute(0, 2, 1).contiguous() dtype = pred_scores.dtype imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w) anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5) # targets batch_size = pred_scores.shape[0] batch_idx = batch['batch_idx'].view(-1, 1) targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1) targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]]) gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0) # pboxes pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4) pred_kpts = self.kpts_decode(anchor_points, pred_kpts.view(batch_size, -1, *self.kpt_shape)) # (b, h*w, 17, 3) _, target_bboxes, target_scores, fg_mask, target_gt_idx = self.assigner( pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype), anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt) target_scores_sum = max(target_scores.sum(), 1) # cls loss # loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way loss[3] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE # bbox loss if fg_mask.sum(): target_bboxes /= stride_tensor loss[0], loss[4] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores, target_scores_sum, fg_mask) keypoints = batch['keypoints'].to(self.device).float().clone() keypoints[..., 0] *= imgsz[1] keypoints[..., 1] *= imgsz[0] for i in range(batch_size): if fg_mask[i].sum(): idx = target_gt_idx[i][fg_mask[i]] gt_kpt = keypoints[batch_idx.view(-1) == i][idx] # (n, 51) gt_kpt[..., 0] /= stride_tensor[fg_mask[i]] gt_kpt[..., 1] /= stride_tensor[fg_mask[i]] area = xyxy2xywh(target_bboxes[i][fg_mask[i]])[:, 2:].prod(1, keepdim=True) pred_kpt = pred_kpts[i][fg_mask[i]] kpt_mask = gt_kpt[..., 2] != 0 loss[1] += self.keypoint_loss(pred_kpt, gt_kpt, kpt_mask, area) # pose loss # kpt_score loss if pred_kpt.shape[-1] == 3: loss[2] += self.bce_pose(pred_kpt[..., 2], kpt_mask.float()) # keypoint obj loss loss[0] *= self.hyp.box # box gain loss[1] *= self.hyp.pose / batch_size # pose gain loss[2] *= self.hyp.kobj / batch_size # kobj gain loss[3] *= self.hyp.cls # cls gain loss[4] *= self.hyp.dfl # dfl gain return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl) def kpts_decode(self, anchor_points, pred_kpts): y = pred_kpts.clone() y[..., :2] *= 2.0 y[..., 0] += anchor_points[:, [0]] - 0.5 y[..., 1] += anchor_points[:, [1]] - 0.5 return y def train(cfg=DEFAULT_CFG, use_python=False): model = cfg.model or 'yolov8n-pose.yaml' data = cfg.data or 'coco8-pose.yaml' device = cfg.device if cfg.device is not None else '' args = dict(model=model, data=data, device=device) if use_python: from ultralytics import YOLO YOLO(model).train(**args) else: trainer = PoseTrainer(overrides=args) trainer.train() if __name__ == '__main__': train()